Molecular Docking

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Molecular Docking
G. Schaftenaar
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Docking Challenge

• Identification of the ligands correct binding
  geometry in the binding site (Binding Mode)
• Observation
• Similar ligands can bind at quite different
  orientations in the active site.

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Two main tasks of Docking Tools

• Sampling of conformational (Ligand) space
• Scoring protein-ligand complexes

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Rigid-body docking algorithms

• Historically the first approaches. 
• Protein and ligand fixed.
• Search for the relative orientation of the two
  molecules with lowest energy.
• FLOG (Flexible Ligands Oriented on Grid) each
  ligand represented by up to 25 low energy
  conformations.

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Introducing flexibilityWhole molecule docking

• Monte Carlo methods (MC)
• Molecular Dynamics (MD)
• Simulated Annealing (SA)
• Genetic Algorithms (GA)
• Available in packages
• AutoDock (MC,GA,SA)
• GOLD (GA)
• Sybyl (MD)

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Monte Carlo

• Start with configuration A (energy EA)
• Make random move to configuration B (energy EB)
• Accept move when
• EB lt EA or if
• EB gt EA except with probability P

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Molecular Dynamics

• force-field is used to calculate forces on each
  atom of the simulated system
• following Newton mechanics, calculate
  accelerations and velocities from the forces.
• (Force mass times acceleration)
• The atoms are moved slightly with respect to a
  given time step

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Simulated Annealing
Finding a global minimium by lowering the
temperature during the Monte Carlo/MD simulation
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Genetic Algorithms

• Ligand translation, rotation and configuration
  variables constitute the genes
• Crossovers mixes ligand variables from parent
  configurations
• Mutations randomly change variables
• Natural selection of current generation based on
  fitness
• Energy scoring function determines fitness

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Introducing flexibility Fragment Based Methods

• build small molecules inside defined binding
  sites while maximizing favorable contacts.
• De Novo methods construct new molecules in the
  site.
• division into two major groups
• Incremental construction (FlexX, Dock)
• Place join.

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Placing Fragments and Rigid Molecules

• All rigid-body docking methods have in common
  that superposition of point sets is a fundamental
  sub-problem that has to be solved efficiently
• Geometric hashing
• Pose clustering
• Clique detection

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Geometric hashing

• originates from computer vision
• Given a picture of a scene and a set of objects
  within the picture, both represented by points in
  2d space, the goal is to recognize some of the
  models in the scene

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Pose-Clustering

• For each triangle of receptor compute the
  transformation to each ligand matching triangle.
• Cluster transformations.
• Score the results.

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Clique-Detection

• Nodes comprise of matches between protein and
  ligand
• Edges connect distance compatible pairs of nodes
• In a clique all pair of nodes are connected

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Scoring Functions

• Shape Chemical Complementary Scores
• Empirical Scoring
• Force Field Scoring
• Knowledge-based Scoring
• Consensus Scoring

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Shape Chemical Complementary Scores

• Divide accessible protein surface into zones
• Hydrophobic
• Hydrogen-bond donating
• Hydrogen-bond accepting
• Do the same for the ligand surface
• Find ligand orientation with best complementarity
  score

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Empirical Scoring

• Scoring parameters fit to reproduce
• Measured binding affinities
• (FlexX, LUDI, Hammerhead)

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Empirical scoring
Loss of entropy during binding
Hydrogen-bonding
Ionic interactions
Aromatic interactions
Hydrophobic interactions
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Force Field Scoring (Dock)
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• Nonbonding interactions (ligand-protein)
• van der Waals
• -electrostatics
• Amber force field

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Knowledge-based Scoring Function

• Free energies of molecular interactions
• derived from structural information on
• Protein-ligand complexes contained in PDB

Boltzmann-Like Statistics of Interatomic Contacts.

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Distribution of interatomic distances is
converted into energy functions by inverting
Boltzmanns law.
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Potential of Mean Force (PMF)
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Number density of atom pairs of type ij at atom
pair distance r
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Number density of atom pairs of type ij in
reference sphere with radius R
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Consensus Scoring

• Cscore
• Integrate multiple scoring functions to
• produce a consensus score that is
• more accurate than any single function
• for predicting binding affinity.

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Virtual screening by Docking

• Find weak binders in pool of non-binders
• Many false positives (96-100)
• Consensus Scoring reduces rate of false positives

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Concluding remarks
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Docking programs

• DOCK
• FlexX
• GOLD
• AutoDOCK
• Hammerhead
• FLOG

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FLEXX

• Receptor is treated as rigid
• Incremental construction algorithm
• Break Ligand up into rigid fragments
• Dock fragments into pocket of receptor
• Reassemble ligand from fragments in low
• energy conformations

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How DOCK works

• Generate molecular surface of protein

Cavities in the receptor are used to define
spheres (blue) the centres are potential
locations for ligand atoms.
Sphere centres are matched to ligand atoms,
to determine possible orientations for the
ligand. 104 orientations generated
thioketal in the HIV1-protease active site
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GOLD(Genetic Optimisation for Ligand Docking)
Performs automated docking with full acyclic
ligand flexibility, partial cyclic ligand
flexibility and partial protein flexibility in
and around active site.
Scoring includes H-bonding term, pairwise
dispersion potential (hydrophobic interactions),
molecular and mechanics term for internal
energy.

• Analysis shows algorithm more likely to fail if
  ligand is large or highly flexible,
• and more likely to succeed if ligand is polar
• The GA is encoded to search for H-bonding
  networks first
• Fitness function contains a term for dispersive
  interactions but takes no account
• of desolvation, thus underestimates
  The Hydrophobic Effect